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Mapping road networks is currently both expensive and labor-intensive. High-resolution aerial imagery provides a promising avenue to automatically infer a road network. Prior work uses convolutional neural networks (CNNs) to detect which pixels belong to a road (segmentation), and then uses complex post-processing heuristics to infer graph connectivity. We show that these segmentation methods have high error rates because noisy CNN outputs are difficult to correct. We propose RoadTracer, a newdoi:10.1109/cvpr.2018.00496 dblp:conf/cvpr/BastaniHAABCMD18 fatcat:t6sum6ydrzdp5frkdo5e4tlc3i